GRAG：面向个性化对话系统的通用响应增强生成框架
GRAG: Generic Response-Augmented Generation Framework for Personalized Conversational Systems
AI总结：
提出GRAG框架，通过离线通用响应解耦内容基础与个性化，使小模型在资源受限环境中专注于个性化注入，性能提升显著。
摘要：
在资源受限或隐私敏感的环境中部署高能力的个性化对话代理仍然是一个重大挑战。我们识别出现有方法中的一个根本瓶颈：当前的训练范式将个性化和基础化视为一个单一的、整体性的学习问题。在这些范式下，语言模型被迫同时解决说什么（内容基础）和如何以用户特定的方式说（个性化），这引入了显著的计算和优化挑战。因此，上下文基础化常常为了个性遵循而被牺牲，反之亦然，导致响应要么在对话历史中基础薄弱，要么个性化不足。在这项工作中，我们提出了通用响应增强生成（GRAG）框架，该框架通过利用来自高容量、通用LLM的离线通用响应作为语义和结构支架，在资源有限的环境中无缝指导更小、任务专用模型的微调，从而解耦这些竞争目标。通过将内容基础化与个性化解耦，GRAG允许模型专注于个性注入，同时牢固地锚定在对话上下文中。我们将GRAG实例化为两种基于后融合和前融合的架构变体，并在多个覆盖不同个性化结构的基准对话数据集上进行评估。我们的结果表明，GRAG显著优于不使用辅助支架的最先进方法，在ROUGE-2上提升高达47%，在BLEU分数上提升36%。最终，GRAG为在资源有限的环境中构建基础感知的个性化对话系统提供了一个可泛化的蓝图。
英文摘要：
Deploying highly capable personalized conversational agents in resource-constrained or privacy-sensitive environments remains a significant challenge. We identify a fundamental bottleneck in the existing approaches: current training paradigms treat personalization and grounding as a single monolithic learning problem. Under these paradigms, language models are forced to simultaneously address what to say (content grounding) and how to say it in a user-specific way (personalization), which introduces significant computational and optimization challenges. Consequently, contextual grounding is often sacrificed for persona adherence, or vice versa, resulting in responses that are either weakly grounded in the conversational history or insufficiently personalized. In this work, we propose the Generic Response-Augmented Generation (GRAG) framework that decouples these competing objectives by leveraging offline, generic responses from high-capacity, general-purpose LLMs as a semantic and structural scaffold to guide the fine-tuning of smaller, task-specialized models seamlessly in resource-limited environments. By decoupling the content grounding from personalization, GRAG allows the model to focus exclusively on persona injection while remaining firmly anchored to the conversational context. We instantiate the GRAG in two post- and pre-fusion-based architectural variants and evaluate them on multiple benchmark conversational datasets that cover diverse personalization structures. Our results demonstrate that GRAG significantly outperforms state-of-the-art methods that do not use auxiliary scaffolding, yielding up to 47% improvements in ROUGE-2 and 36% in BLEU scores. Ultimately, GRAG offers a generalizable blueprint for building grounding-aware personalized conversational systems in resource-limited environments.
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